Settlement maps derived by Earth Observation data represent a critical dataset for building stock quantification.The accuracy of the settlement maps varies across the different spatial scales and across the space acco...Settlement maps derived by Earth Observation data represent a critical dataset for building stock quantification.The accuracy of the settlement maps varies across the different spatial scales and across the space according to specific spatial patterns.The aim of this paper is to assess the accuracy of the settlement map at different scales,and to analyze the relationships between spatial allocation of error and built-up distribution patterns.The paper identifies two general trends.First that the building stock overestimation error increases with increasing values of spatial scattering.Second that at coarser scales the relation between building area overestimation and spatial scattering became stronger.The results have important implications when settlement maps are used to estimate the building stock.展开更多
Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) conce...Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types(square and circular) and 6 representative algorithms(Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods(i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of firstand second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30% and 9.68%.The SOC maps resulting from the higher-order algorithms(Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.展开更多
文摘Settlement maps derived by Earth Observation data represent a critical dataset for building stock quantification.The accuracy of the settlement maps varies across the different spatial scales and across the space according to specific spatial patterns.The aim of this paper is to assess the accuracy of the settlement map at different scales,and to analyze the relationships between spatial allocation of error and built-up distribution patterns.The paper identifies two general trends.First that the building stock overestimation error increases with increasing values of spatial scattering.Second that at coarser scales the relation between building area overestimation and spatial scattering became stronger.The results have important implications when settlement maps are used to estimate the building stock.
基金supported by the National Natural Science Foundation of China(Nos.91325301,41571130051,41401237,41571212,and 41371224)partly by the Jiangsu Provincial Science Foundation for Youths,China(No.BK20141053)
文摘Local terrain attributes,which are derived directly from the digital elevation model,have been widely applied in digital soil mapping.This study aimed to evaluate the mapping accuracy of soil organic carbon(SOC) concentration in 2 zones of the Heihe River in China,by combining prediction methods with local terrain attributes derived from different polynomial models.The prediction accuracy was used as a benchmark for those who may be more concerned with how accurately the variability of soil properties is modeled in practice,rather than how morphometric variables and their geomorphologic interpretations are understood and calculated.In this study,2 neighborhood types(square and circular) and 6 representative algorithms(Evans-Young,Horn,Zevenbergen-Thorne,Shary,Shi,and Florinsky algorithms) were applied.In general,35 combinations of first-and second-order derivatives were produced as candidate predictors for soil mapping using two mapping methods(i.e.,kriging with an external drift and geographically weighted regression).The results showed that appropriate local terrain attribute algorithms could better capture the spatial variation of SOC concentration in a region where soil properties are strongly influenced by the topography.Among the different combinations of firstand second-order derivatives used,there was a best combination with a more accurate estimate.For different prediction methods,the relative improvement in the two zones varied between 0.30% and 9.68%.The SOC maps resulting from the higher-order algorithms(Zevenbergen-Thorne and Florinsky) yielded less interpolation errors.Therefore,it was concluded that the performance of predictive methods,which incorporated auxiliary variables,could be improved by attempting different terrain analysis algorithms.
